150 Years of Nature

Node-links - Association

Photo by JJ Ying on Unsplash

…to ensure that the results of science are rapidly disseminated to the public throughout the world,
in a fashion that conveys their significance for knowledge, culture and daily life…
— Nature’s mission statement

An analysis of Nature magazine’s co-citation network comprised of more than 88,000 papers published since 1900.

Ingest

nodes

df_nodes <- read.csv("archetypes/150-yrs-of-nature/cocite-nodes.csv", header = TRUE, stringsAsFactors = FALSE)
df_nodes <- df_nodes %>% select(NatureID, Title, PubYear)
df_nodes <- df_nodes[!duplicated(df_nodes), ]
df_nodes

Ingest

edges

df_edges <- read.csv("archetypes/150-yrs-of-nature/cocite-edges.csv", header = TRUE, stringsAsFactors = FALSE)
df_edges

Wrangle

filter by publication year

df_nodes_filtered <- filter(df_nodes, PubYear > 2014)
df_nodes_filtered

df_edges_filtered <- filter(df_edges, source %in% df_nodes_filtered$NatureID & target %in% df_nodes_filtered$NatureID) 
# df_edges_filtered

Wrangle

transform to a graph data frame and remove isolates

df_graph <- graph_from_data_frame( df_edges_filtered, directed = FALSE, vertices = df_nodes_filtered )

# remove isolates
isolates <- which(degree(df_graph)==0)
df_graph <- delete.vertices(df_graph, isolates)

# df_graph

Analyze

calculate centrality

Creating numerical fields for the Nodes set in order to attribute the nodes size and/or color

## Creating centrality numerical field for the Nodes

# centrality_alpha(), centrality_authority(), centrality_betweenness() 
# centrality_power(), centrality_closeness(), centrality_eigen() 
# centrality_hub(), centrality_pagerank(), centrality_subgraph() 
# centrality_degree(), centrality_edge_betweenness(), centrality_manual() 
# centrality_closeness_harmonic(), centrality_closeness_residual() 
# centrality_closeness_generalised() 
# centrality_communicability() , centrality_communicability_odd() 
# centrality_communicability_even() 
# centrality_subgraph_odd(), centrality_subgraph_even() 
# centrality_katz(), centrality_betweenness_network() 
# centrality_betweenness_current(), centrality_betweenness_communicability() 
# centrality_betweenness_rsp_simple(), centrality_betweenness_rsp_net() 
# centrality_information(), centrality_decay() 
# centrality_random_walk(), centrality_expected()

cg <- df_graph %>% 
    as_tbl_graph() %>% 
    activate(nodes) %>% 
    # associating random numerical value to nodes to differentiate their colors and sizes
    mutate(centrality  = centrality_betweenness())

# cg

Analyze

calculate groups

## Creating group numerical field for the Nodes

# group_components(), group_edge_betweenness() 
# group_fast_greedy(), group_infomap() 
# group_label_prop(), group_leading_eigen() 
# group_louvain(), group_optimal() 
# group_spinglass(), group_walktrap() 
# group_biconnected_component()

cg <- cg %>% 
  activate(nodes) %>%
  # use functions to compute numerical values to attribute the edge width and/or color
  mutate(group = group_louvain())

# cg

View

calculate layout for coordinates

# Layouts can be computationally intensive
# Un-comment only for use

# igraph
# layout <- create_layout(cg, layout = 'dh')
# layout <- create_layout(cg, layout = 'fr')
# layout <- create_layout(cg, layout = 'gem')
# layout <- create_layout(cg, layout = 'lgl')
# layout <- create_layout(cg, layout = 'sugiyama')
# layout <- create_layout(cg, layout = 'star')
# layout <- create_layout(cg, layout = 'kk', maxiter = 100)
# layout <- create_layout(cg, layout = 'drl')
# layout <- create_layout(cg, layout = 'linear')
# layout <- create_layout(cg, layout = 'linear' , circular = TRUE)

# ggraph
# layout <- create_layout(cg, layout = 'linear')
# layout <- create_layout(cg, layout = 'linear', circular = TRUE)
layout <- create_layout(cg, layout = 'stress')
# layout <- create_layout(cg, layout = 'sparse_stress')
# layout <- create_layout(cg, layout = 'backbone')
# layout <- create_layout(cg, layout = 'pmds')
# layout <- create_layout(cg, layout = 'eigen')
# layout <- create_layout(cg, layout = 'centrality')
# layout <- create_layout(cg, layout = 'focus')
# layout <- create_layout(cg, layout = 'dendrogram')
# layout <- create_layout(cg, layout = 'unrooted')
# layout <- create_layout(cg, layout = 'matrix')
# layout <- create_layout(cg, layout = 'hive')
# layout <- create_layout(cg, layout = 'fabric')

# graphlayouts
# layout <- create_layout(cg, layout = "nicely")
# layout <- create_layout(cg, layout = "stress")
# layout <- create_layout(cg, layout = "focus", focus = 1)

View

edge aesthetics

# Edge Types
# geom_edge_arc, geom_edge_arc0, geom_edge_arc2
# geom_edge_density, geom_edge_diagonal, geom_edge_diagonal0, geom_edge_diagonal2
# geom_edge_elbow, geom_edge_elbow0, geom_edge_elbow2
# geom_edge_fan, geom_edge_fan0, geom_edge_fan2
# geom_edge_hive, geom_edge_hive0, geom_edge_hive2
# geom_edge_link, geom_edge_link0, geom_edge_link2
# geom_edge_loop, geom_edge_loop0

View

apply layout and aesthetics

theme_opts <- theme(
    text = element_text(family = "inconsolata"), 
    legend.position='none'
)

v1 <- ggraph(layout) + 
  geom_edge_link() +
  geom_point_interactive(aes(x = x, y = y, color = group, size = centrality, tooltip = Title, data_id = name)) +
  scale_color_viridis(option = "plasma") +
  scale_size(range = c(1,12)) +
  theme_graph() +
  theme_opts

girafe(ggobj = v1, width_svg = 16, height_svg = 16,
       options = list(opts_sizing(rescale = TRUE, width = 1.0))
)

References

citations for narrative and data sources

Narrative and Data: